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Denoising Processing of Heart Sound Signal Based on Wavelet Transform

机译：基于小波变换的心音信号降噪处理

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摘要

When the heart sounds reach the chest wall surface through mediated tissue, it is prone to generate noise, which can reduce the accuracy of pathological diagnosis. A new denoising method for heart sound signal based on wavelet transform is proposed. First of all, the information signal is transformed by multi-scale wavelet. The wavelet coefficients of each scale indicate a distribution sequence of probability according to the corresponding wavelet entropy threshold to find the maximum entropy of wavelet on certain interval, and the interval is recognized as the leading range of noise. And then, a fixed threshold denoising method is used to adaptively enhance the judgment about absolute value with larger attenuation wavelet coefficients, which can reduce the high frequency sound signal loss and improve the heart sound signal to noise ratio. The denoising simulation experiment is carried out to test the performance. The result shows that the proposed method can improve the output signal to noise ratio of heart sound signal, reducing the influence of noise on the extraction of heart sound signal, and therefore, the noise elimination algorithm has stronger anti-interference ability and superior performance.
机译：当心音通过介导的组织到达胸壁表面时，容易产生噪音，这会降低病理诊断的准确性。提出了一种基于小波变换的心音信号降噪新方法。首先，信息信号通过多尺度小波变换。每个尺度的小波系数根据对应的小波熵阈值指示概率的分布顺序，以求出一定间隔上的小波的最大熵，并将该间隔视为噪声的领先范围。然后，采用固定的阈值去噪方法，以较大的衰减小波系数自适应地增强对绝对值的判断，可以减少高频声信号的损失，提高心音信噪比。进行降噪模拟实验以测试其性能。结果表明，该方法可以提高心音信号的输出信噪比，减少噪声对心音信号提取的影响，因此，该消噪算法具有较强的抗干扰能力和优越的性能。

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